high-speed zones, installing pedestrian detection systems on buses and setting special bus lanes in crowded areas.Traffic crashes could result in severe outcomes such as injuries and deaths. Thus, understanding factors associated with crash severity is of practical importance. Few studies have deeply examined how prior violation and crash experience of drivers and roadways are associated with crash severity.
In this study, a set of risk indicators of road users and roadways were developed based on their prior violation and crash records (e.g., cumulative crash frequency of a roadway), in order to reflect certain aspect or degree of their driving risk. To explore the impacts of those indicators on crash severity and complex interactions among all contributing factors, a Bayesian network approach was developed, based on citywide crash data collected in Kunshan, China from 2016 to 2018. A variable selection procedure based on Information Value (IV) was developed to identify significant variables, and the Bayesian network was employed to explicitly explore statistical associations between crash severity and significant v performance.Research on risk for child pedestrian injury risk focuses primarily on cognitive risk factors, but emotional states such as fear may also be relevant to injury risk. The current study examined children's perception of fear in various traffic situations and the relationship between fear perception and pedestrian decisions.
150 children aged 6-12-years old made pedestrian decisions using a table-top road model. Their perceived fear in the pedestrian context was assessed.
Children reported greater emotional fear when they faced quicker traffic, shorter distances from approaching traffic, and red rather than green traffic signals. https://www.selleckchem.com/products/wst-8.html Children who were more fearful made safer pedestrian decisions in more challenging traffic situations. However, when the least risky traffic situation was presented, fear was associated with more errors in children's pedestrian decisions fearful children failed to cross the street when they could have done so safely. Perception of fear did not vary by child age, although safe pedestrian decisions were more common among the older children.
Children's emotional fear may predict risk-taking in traffic. When traffic situations are challenging to cross within, fear may appropriately create safer decisions. However, when the traffic situation is less risky, feelings of fear could lead to excessive caution and inefficiency. Practical applications Child pedestrian safety interventions may benefit by incorporating activities that introduce realistic fear of traffic risks into broader safety lessons.
Children's emotional fear may predict risk-taking in traffic. When traffic situations are challenging to cross within, fear may appropriately create safer decisions. However, when the traffic situation is less risky, feelings of fear could lead to excessive caution and inefficiency. Practical applications Child pedestrian safety interventions may benefit by incorporating activities that introduce realistic fear of traffic risks into broader safety lessons.Predicting crash counts by severity plays a dominant role in identifying roadway sites that experience overrepresented crashes, or an increase in the potential for crashes with higher severity levels. Valid and reliable methodologies for predicting highway accidents by severity are necessary in assessing contributing factors to severe highway crashes, and assisting the practitioners in allocating safety improvement resources.
This paper uses urban and suburban intersection data in Connecticut, along with two sophisticated modeling approaches, i.e. a Multivariate Poisson-Lognormal (MVPLN) model and a Joint Negative Binomial-Generalized Ordered Probit Fractional Split (NB-GOPFS) model to assess the methodological rationality and accuracy by accommodating for the unobserved factors in predicting crash counts by severity level. Furthermore, crash prediction models based on vehicle damage level are estimated using the same two methodologies to supplement the injury severity in estimating crashes by severity whindings of this research could help select sound and reliable methodologies for predicting highway accidents by injury severity. When crash data samples have challenges associated with the low observed sampling rates for severe injury crashes, this research also confirmed that vehicle damage can be appropriate as an alternative to injury severity in crash prediction by severity.In this study we explore the added value of bicycle crash descriptions from open text fields in hospital records from the Aarhus municipality in Denmark. We also explore how bicycle crash data from the hospital complements crash data registered by the police in the same area and time period.
The study includes 5,313 Danish bicycle crashes, of which 4,205 were registered at the hospital and 1,078 by the police. All crashes occurred from 2010 to 2015. We performed an in-depth analysis of the open text fields on hospital records to identify factors associated with each crash using four categories bicyclist, road, bicycle, and the other party. We employed the chi-squared test to compare the distribution of variables between crashes registered at the hospital and by the police. A binary logit model was used to estimate the probability that a crash factor is identified, and that each crash factor is associated with a single-bicycle crash.
The open-ended text fields in hospital records provide detailed informaut crash-associated factors as well as information about a larger number of bicycle crashes, particularly single-bicycle crashes. Practical implication Efforts to improve access to detailed information about bicycle crashes are needed to provide a better basis for bicycle crash prevention.Falls among older adults are a significant health concern affecting more than a quarter of older adults (age 65+). Certain fall risk factors, such as medication use, increase fall risk among older adults (age 65+).
The aim of this study is to examine the association between antidepressant-medication subclass use and self-reported falls in community-dwelling older adults.
This analysis used the 2009-2013 Medicare Current Beneficiary Survey, a nationally representative panel survey. A total of 8,742 community-dwelling older adults, representing 40,639,884 older Medicare beneficiaries, were included. We compared self-reported falls and psychoactive medication use, including antidepressant subclasses. These data are controlled for demographic, functional, and health characteristics associated with increased fall risk. Descriptive analyses and multivariate logistic regression analyses were conducted using SAS 9.4 and Stata 15 software.
The most commonly used antidepressant subclass were selective serotonin reuptake inhibitors (SSRI) antidepressants (13.